Yiyang (Ian) Wang, Ph.D.
Assistant Professor
- Milwaukee WI UNITED STATES
- Electrical Engineering and Computer Science
Yiyang (Ian) Wang's research interests include machine learning, data science, and medical informatics.
Education, Licensure and Certification
Ph.D.
Computer and Information Sciences
DePaul University
2023
M.S.
Computer Science
DePaul University
2018
Biography
Areas of Expertise
Event and Speaking Appearances
Lung Nodule Malignancy Subtype Discovery with Semantic Learning
26th International Conference on Pattern Recognition (ICPR) Montreal, QC, Canada
2021-08-21
Explainable Deep Learning for Biomarker Classification of OCT Images
20th IEEE International Conference on BioInformatics and BioEngineering Virtual Conference
2020-10-26
Selected Publications
No nodule left behind: evaluating lung nodule malignancy classification with different stratification schemes
Medical Imaging 2023: Computer-Aided Diagnosis2023
Machine learning models have been widely used in lung cancer computer-aided diagnosis (CAD) studies. However, the heterogeneity in the visual appearance of lung nodules as well as lack of consideration of hidden subgroups in the data are significant obstacles to generating accurate CAD outcomes across all nodule instances. Previous lung cancer CAD models aim to achieve Empirical Risk Minimization (ERM), which leads to a high overall accuracy but often fails at predicting certain subgroups caused by the lung cancer heterogeneity.
Lung Nodule Malignancy Subtype Discovery with Semantic Learning
2022 26th International Conference on Pattern Recognition (ICPR)2022
Computer-aided diagnosis (CAD) systems have been widely used as second readers in lung cancer diagnosis. However, lung cancer heterogeneity and lack of using human annotated semantic characteristics are significant obstacles to an accurate and explainable CAD outcome. We propose a novel CAD scheme that characterizes lung nodule malignancy subtypes semantically and classifies nodule malignancy through a semantic learning process. We built and evaluated our method on a publicly available dataset, Lung Image Database Consortium (LIDC).
Autorevise: annotation refinement using motion signal patterns
SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing2022
Annotating a video for activity recognition, when precise, frame-level activity localization is required, is time-consuming and difficult to accomplish with high accuracy. We propose a novel Autorevise approach for motion-based pattern recognition for improving the accuracy of activity labels of video data. This paper applies signal processing methods to motion features (e.g. speed of a subject as observed in the video) in order to identify shapes in the signals associated with activities to be classified.
Drusen segmentation with sparse volumetric SD-OCT sampling
Medical Imaging 2021: Image Processing2021
Age-Related Macular Degeneration (AMD) is a common eye disease characterized by the build-up of drusen, small deposits of extracellular materials in the macula. Early detection of drusen is key to understanding the progression of AMD. Therefore, accurate and robust segmentation of drusen during AMD progression is important for automated detection, classification, diagnosis, and prognosis tasks. Spectral-domain optical coherence tomography (SD-OCT) is a popular macular imaging modality used for these tasks.
